Predicting PACS loading and performance metrics using Monte Carlo and queuing methods

Paul Nagy, Michael Warnock, Damien Evans

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Determining the performance bottleneck of a PACS system is a challenging task. System performance is dependent on several variables such as the workstation, network, servers, type of data, and different loading conditions. This makes planning difficult to ensure the system capacity will deliver fast access to images throughout the enterprise of a hospital even during rush periods. The rules of thumb that most vendors use for the number of workstations per server are based upon heuristic experience and may not apply from institution to institution where usage and infrastructures are different. Rules of thumb can be problematic and usually cannot predict the impact when new technology is introduced like Gigabit Ethernet or distributed architectures. We have developed a Monte Carlo Model in an attempt to develop a more accurate model to predict loading on a system at peak "rush hour" times. The focus of the model was on user metrics of performance such as the latency and throughput of images to their workstation. Analysis demonstrates that "traffic jams" can occur and dissipate in a matter of minutes and be relatively irreproducible to the PACS administrator.

Original languageEnglish (US)
Pages (from-to)48-56
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Sep 15 2003
Externally publishedYes
EventMedical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation - San Diego, CA, United States
Duration: Feb 18 2003Feb 20 2003


  • Computer simulation
  • Monte Carlo
  • PACS
  • Performance modeling

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Predicting PACS loading and performance metrics using Monte Carlo and queuing methods'. Together they form a unique fingerprint.

Cite this